Nonparametric Density and Regression Estimation

John DiNardo and Justin Tobias ()

Journal of Economic Perspectives, 2001, vol. 15, issue 4, 11-28

Abstract: We provide a nontechnical review of recent nonparametric methods for estimating density and regression functions. The methods we describe make it possible for a researcher to estimate a regression function or density without having to specify in advance a particular--and hence potentially misspecified functional form. We compare these methods to more popular parametric alternatives (such as OLS), illustrate their use in several applications, and demonstrate their flexibility with actual data and generated-data experiments. We show that these methods are intuitive and easily implemented, and in the appropriate context may provide an attractive alternative to "simpler" parametric methods.

JEL-codes: C14 C20 C22 J24 (search for similar items in EconPapers)
Date: 2001
Note: DOI: 10.1257/jep.15.4.11
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Citations: View citations in EconPapers (145)

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